Pavement performance prediction through fuzzy regression

Authors:
Nang-Fei Pan;Chien-Ho Ko;Ming-Der Yang;Kai-Chun Hsu
Affiliations:
Department of Civil Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan;Department of Civil Engineering, National Pingtung University of Science and Technology, 1, Shuefu Road, Neipu, Pingtung 912, Taiwan;Department of Civil Engineering, National Chung Hsing University, 250, Kuo Kuang Road, Taichung 402, Taiwan;Department of Civil Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Accurate predictions of future pavement conditions are essential for determining the most cost-effective maintenance strategy. The current methods for assessing pavement conditions involve either equipment measures or visual inspections. Equipment measures are not extensively implemented because of high cost; thus, subjective evaluations by road inspectors are often used as a replacement. Nevertheless, visual inspections could draw in errors and variations due to subjectivity and uncertainty. The present serviceability index (PSI), one of the most common indicators used to evaluate pavement performance, is incapable of transforming one's imprecise judgment into an exact number between 0 (the worst) and 5 (the best). Conventional regression cannot deal with visual inspection data that are linguistic or non-crisp. In contrast, fuzzy regression is capable of handling such fuzzy data. In this paper, pavement conditions are exemplified by five membership functions and estimated by using fuzzy regression to better account the uncertainties of the traditional method. Also, a similarity indicator is applied to measure the goodness of fit. A case study using pavement inspection data is presented to establish estimated fuzzy regression equations. The results demonstrate the capability of the model, which is able to assist road administration units to determine desirable repair actions regarding the predicted pavement conditions.